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Related Experiment Video

Updated: Jan 9, 2026

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor

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Published on: May 23, 2025

Assessing Learning-Based Reconstructed Liver Surfaces From Partial Point Clouds for Improving Pre- to Intra-Operative

Nakul Poudel¹, Zixin Yang¹, Richard Simon²

¹Center for Imaging Science Rochester Institute of Technology Rochester New York USA.

Healthcare Technology Letters

|December 11, 2025

Summary

This summary is machine-generated.

Generating complete liver surfaces from partial intra-operative data improves surgical registration. Errors in generated surfaces impact both visible and invisible regions, highlighting the need for careful consideration in image-guided liver surgery.

Keywords:

image‐guided liver surgery registration surface completion uncertainty assessment

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Area of Science:

Medical Imaging
Computer-Aided Surgery
Geometric Deep Learning

Background:

Image-guided liver surgery requires accurate fusion of pre-operative and intra-operative data.
Partial visibility of the liver during surgery poses a significant challenge for registration.
Learning-based methods for generating complete surfaces from partial point clouds offer potential solutions.

Purpose of the Study:

To analyze the error introduced by generating complete intra-operative liver surfaces from partial data.
To evaluate the impact of this generated surface error on both rigid and non-rigid registration algorithms.
To ensure robust performance of image-guided liver surgery in clinical settings.

Main Methods:

Utilized a VN-OccNet framework trained on simulated deformed liver data.
Generated complete surfaces from partial point clouds acquired from multiple viewpoints of in vitro liver phantoms.
Integrated generated surfaces into Go-ICP and GMM-FEM registration frameworks.
Estimated registration errors in both visible and invisible regions.

Main Results:

Surface generation error increases with distance from the visible partial surface.
Generated surface errors impact registration accuracy in invisible regions.
Registration errors were also observed in visible regions, affecting accuracy within the camera's field of view.

Conclusions:

Understanding and quantifying surface generation error is crucial for accurate image-guided liver surgery.
The proposed method provides insights into error propagation during registration with generated surfaces.
Further refinement is needed to mitigate errors in both visible and invisible regions for clinical application.